The emergence of large language models (LLMs) has revolutionized our understanding of artificial intelligence and its relationship to human cognition. Recent research by AlKhamissi et al. has revealed striking insights into how LLMs develop brain-like representations during training, yet ultimately transcend the limitations of human language processing networks. This essay examines the fundamental differences between human brain networks and computer networks in language processing, analyzing their respective contributions to linguistic competence and performance. Through a comprehensive analysis of formal versus functional linguistic competence, neural scaling laws, and computational architectures, we explore how LLMs achieve superhuman language capabilities while maintaining surprising alignment with human brain activity.

The Human Language Network: Architecture and Functions

The human language network represents one of evolution’s most sophisticated information processing systems. Located primarily in the left hemisphere’s frontal and temporal regions, this network includes Broca’s area (inferior frontal gyrus), Wernicke’s area (posterior superior temporal gyrus), and the anterior temporal lobe, which serves as a semantic hub. These regions are interconnected through dense white matter pathways that enable rapid, parallel processing of linguistic information. The anterior temporal lobe functions as a critical semantic hub, integrating multimodal conceptual information and supporting the retrieval of word meanings. This region exhibits bilateral activation during language processing, particularly in tonal languages like Chinese, where both hemispheres contribute to pitch and semantic processing. The language network’s hierarchical organization allows for the processing of linguistic information at multiple levels, from phonological features to complex semantic relationships.

Surprisingly, the human brain operates only approximately 12 watts of power, demonstrating remarkable energy efficiency compared to artificial systems. This efficiency stems from several unique characteristics, including analog and recurrent processing, and extremely parallel architecture (i.e. many many-core!). Unlike digital computers, the brain processes information through continuous, analog signals that allow for probabilistic and context-sensitive computations. This analog nature enables the brain to handle ambiguity and uncertainty inherent in natural language with remarkable flexibility. The human brain also appears to analyze language serially, word by word, with extensive recurrent connections that enable temporal integration and context updating. Even though the human language network is constrained by biological limitations including processing speed, memory capacity, and energy consumption, these constraints have shaped the evolution of efficient, specialized processing mechanisms optimized for natural language communication. For example, the brain’s massively parallel architecture in cellular level allows simultaneous processing of multiple linguistic dimensions. Approximately 100 billion neurons organized in layered cortical structures enable hierarchical processing from simple pattern detection to complex semantic relationships.

The Phenomenon of Outgrowing Brain Alignment in Computers

The AlKhamissi et al. study’s analysis of 34 training checkpoints spanning 300 billion tokens reveals a fascinating trajectory of brain-model alignment. Initially, as LLMs develop formal linguistic competence, their representations become increasingly similar to human brain activity. This alignment peaks during the acquisition of core linguistic rules and syntactic patterns. However, as models continue training and develop capabilities beyond human proficiency, brain alignment begins to plateau and eventually decline. This “outgrowing” phenomenon suggests that optimal language processing for artificial systems diverges from the constraints and solutions evolved by biological systems. Investigating whether enhanced brain alignment could improve model performance in specific domains might lead to novel training objectives that combine statistical learning with neuroscience-inspired constraints.

Additionally, studying the specific linguistic capabilities that emerge as models outgrow brain alignment could illuminate the unique advantages of artificial processing systems and identify areas where biological constraints may actually limit optimal performance. Understanding the complementary strengths of biological and artificial language processing leads to more capable, efficient, and human-compatible language processing systems while advancing our fundamental understanding of intelligence, communication, and cognition.